WO2002023523A2 - Synchronisation rapide de la forme d'onde pour la concatenation et la modification a echelle de temps de la parole - Google Patents

Synchronisation rapide de la forme d'onde pour la concatenation et la modification a echelle de temps de la parole Download PDF

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Publication number
WO2002023523A2
WO2002023523A2 PCT/US2001/028672 US0128672W WO0223523A2 WO 2002023523 A2 WO2002023523 A2 WO 2002023523A2 US 0128672 W US0128672 W US 0128672W WO 0223523 A2 WO0223523 A2 WO 0223523A2
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Prior art keywords
speech
waveform
concatenation
concatenation system
segments
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PCT/US2001/028672
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English (en)
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WO2002023523A3 (fr
Inventor
Geert Coorman
Bert Van Coile
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Lernout & Hauspie Speech Products N.V.
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Priority to EP01970936A priority Critical patent/EP1319227B1/fr
Priority to AU2001290882A priority patent/AU2001290882A1/en
Priority to DE60127274T priority patent/DE60127274T2/de
Publication of WO2002023523A2 publication Critical patent/WO2002023523A2/fr
Publication of WO2002023523A3 publication Critical patent/WO2002023523A3/fr

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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/04—Time compression or expansion
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/06—Elementary speech units used in speech synthesisers; Concatenation rules
    • G10L13/07—Concatenation rules

Definitions

  • the present invention relates to speech synthesis, and more specifically, changing the speech rate of sampled speech signals and concatenating speech segments by efficiently joining them in the time-domain.
  • Speech segment concatenation is often used as part of speech generation and modification algorithms.
  • TTS Text-To-Speech
  • TMS Time Scale Modification
  • junctions between speech segments are a possible source of degradation in speech quality. Thus, signal discontinuities at each junction should be minimized.
  • Speech segments can be concatenated either in the time-, frequency- or time-frequency-domain.
  • the present invention is about time-domain concatenation (TDC) of digital speech waveforms.
  • TDC time-domain concatenation
  • High quality joining of digital speech waveforms is important in a variety of acoustic processing applications, including concatenative text-to-speech (TTS) systems such as the one described in U.S. Patent Application 09/438,603 by G. Coorman et al.; broadcast message generation as described, for example, in L.F. Lamel, J.L.
  • TDC avoids computationally expensive transformations to and from other domains, and has the further advantage of preserving intrinsic segmental information in the waveform.
  • the natural prosodic information (including the micro-prosody — one of the key factors for highly natural sounding speech) is transferred to the synthesized . speech.
  • One major concern of TDC is to avoid audible waveform irregularities such as discontinuities and transients that may occur in the neighborhood of the join. These are commonly referred as "concatenation artifacts".
  • two speech segments can be joined together by fading-out the trailing edge of the left segment and fading-in the leading edge of the right segment before overlapping and adding them.
  • smooth concatenation is done by means of weighted overlap-and-add, a technique that is well known in the art of digital speech processing.
  • Such a method has been disclosed in U.S. Patent No. 5,490,234 by Narayan, incorporated herein by reference.
  • the length of the speech segments involved depends on the application. Small speech segments (e.g. speech frames) are typically used in time-scale modification applications while longer segments such as diphones are used in text-to-speech applications and even longer segments can be used in domain specific applications such as carrier slot applications.
  • Some known waveform synchronization techniques address waveform similarity as described in W. Verhelst & M. Roelands, "An Overlap-Add Technique Based on Waveform Similarity (WSOLA) for High Quality Time-Scale Modification of Speech/' ICASSP-93. IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 554-557, Vol. 2, 1993; incorporated herein by reference.
  • W. Verhelst & M. Roelands "An Overlap-Add Technique Based on Waveform Similarity (WSOLA) for High Quality Time-Scale Modification of Speech/' ICASSP-93. IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 554-557, Vol. 2, 1993; incorporated herein by reference
  • a common method of synthesizing speech in text-to-speech (TTS) systems is by combining digital speech waveform segments extracted from recorded speech that are stored in a database. These segments are often referred in speech processing literature as "speech units".
  • a speech unit used in a text-to-speech synthesizer is a set consisting of a sequence of samples or parameters that can be converted to waveform samples taken from a continuous chunk of sampled speech and some accompanying feature vectors (containing information such as prominence level, phonetic context, pitch%) to guide the speech unit selection process, for example.
  • Some common and well described representations of speech units used in concatenative TTS systems are frames as described in R. Hoory & D.
  • a TD-PSOLA synthesizer concatenates windowed speech segments centered on the instant of glottal closure (GCI) that have a typical duration of two pitch periods.
  • GCI glottal closure
  • a technique which aims to avoid such problems is the MBROLA synthesis method that is described in T. Dutoit & H. Leich, "MBR-PSOLA: Text-to-Speech Synthesis Based on an MBE Re- Synthesis of the Segments Database” , Speech Communication, Vol. 13, pages 435- 440, incorporated herein by reference.
  • the MBROLA technique pre-processes the segments of the inventory by equalization of the pitch period over the complete segment database and by resetting the low frequency phase components to a pre-defined value. This technique facilitates spectral interpolation.
  • MBROLA has the same computational efficiency as PSOLA and its concatenation is smoother. However MBROLA makes the synthesized speech more metallic sounding because of the pitch-synchronous phase resets.
  • the present invention provides an apparatus for concatenating a first quasi-periodic digital waveform segment with a second quasi-periodic digital waveform segment, such that the trailing part of the first waveform segment and leading part of the second waveform segment are concatenated smoothly.
  • the concatenation is done by means of overlap-and-add, a technique well known in the art of speech processing.
  • the waveform synchronizer /concatenator determines an optimum blend point for the first and second digital waveform segments in order to minimize audible artifacts near the join.
  • the waveform regions centered around the optimal blend points are overlapped in time and added to generate a digital waveform sequence representing a concatenation of the first and second digital waveform segment.
  • the technique is applicable to concatenate any two quasi-periodic waveforms, commonly encountered in the synthesis of sound, voiced speech, music or the like.
  • Figure 1 gives a general functional view of the waveform synchronization mechanism embedded in a waveform concatenator.
  • Figure 2 gives a general functional view of the waveform synchronizer and blender.
  • Figure 3 shows the typical shapes of the fade-in and fade-out functions that are used in the waveform blending process.
  • Figure 4 shows how the blending anchor is calculated based on some features of the signal in the neighborhood of the join.
  • the concatenated signal y(n) is analyzed in the neighborhood of the join.
  • index L corresponds with the time-index of the join , and it is also assumed that the distortion to the left and to the right of the join have the same importance (i.e. same weight).
  • y(n) is a mixture of x l (n) and x 2 (n) .
  • the signal y(n) toward the left side of the concatenation zone corresponds to part of the segment extracted from x x (n), and toward the right side of the concatenation zone corresponds to part of the segment extracted from the signal x 2 (n) .
  • aconcatenation point is selected, based on a synchronization measure, from a set of potential concatenation points that lay in a (small) time interval called the optimization zone.
  • the optimization zone is typically located at the edges of the speech segments (where the concatenation should take place).
  • a short- time (ST) Fourier spectrum Y( ⁇ , L-D) of y(n) is expected that closely resembles that of X ⁇ ( ⁇ , E x - D), the ST Fourier spectrum of x l (n) around E l .
  • ST spectrum Y( ⁇ , L + D) is expected that closely resembles X 2 ( ⁇ ,E 2 + D), the ST spectrum of x 2 (n) around time-index E 2 .
  • the spectral distortion may be defined as the mean squared error between the spectra:
  • w(n) is the window (e.g. Blackman window) that was used to derive the short-time Fourier transform.
  • minimization of the concatenation artifacts can be performed by minimizing the weighted mean square error. This can be further expanded in terms of energy as follows:
  • Equation (5) can be further simplified if the window w ⁇ n) is chosen to be the following trigonometric window:
  • the minimization of the distortion ⁇ is shown to be a compromise between the minimization of the energy of the weighted segment at the left and right side of the join (i.e. first two terms) and the maximization of the cross-correlation between the left and the right weighted segment (third term).
  • the distortion minimization in the least mean square sense is interesting because it leads to an analytical representation that delivers insight into the problem solution.
  • the distortion as it is defined here does not take into account perceptual aspects such as auditory masking and non-uniform frequency sensitivity.
  • the minimization of the three terms in equation (7) is equivalent to the maximization of the cross- correlation only (i.e. waveform similarity condition), while if the two waveform segments are uncorrelated, the best optimization criterion that can be chosen is the energy minimization in the neighborhood of the join.
  • the distortion represented by equation (7) is composed as a sum of three different energy terms.
  • the first two terms are energy terms while the third term is a "cross-energy" term. It is well known that representing the energy in the logarithmic domain rather than in the linear domain better corresponds to the way humans perceive loudness. In order to weight the energy terms approximately perceptually equally, the logarithm of those terms may be taken individually.
  • the concatenation of the two segments can be readily expressed in the well-known weighted overlap-and-add (OLA) representation.
  • OLA weighted overlap-and-add
  • the short time fade-in/ fade-out of speech segments in OLA will be further referred to as waveform blending.
  • the time interval over which the waveform blending takes place is referred to as the concatenation zone.
  • two indices E 0pt and E° pt are obtained that will be called the optimal blending anchors for the first and second waveform segments respectively.
  • the two blending anchors E x and E 2 vary over an optimization interval in the trailing part of the first waveform segment and in the leading part of the second waveform segment respectively such that the spectral distortion due to blending is minimized according to a given criterion; for example, maximizing the normalized cross- correlation of equation (8).
  • the trailing part of the first speech segment and the leading part of the second speech segment are overlapped in time such that the optimal blending anchors coincide.
  • the waveform blending itself is then achieved by means of overlap-and-add, a technique well known in the art of speech processing.
  • the distance D from the left side of the join is chosen to be approximately equal to the average pitch period P derived from the speech database from which the waveforms x x (n) and x 2 (n) were taken.
  • the optimization zones over which E x and E 2 vary are also of the order of P.
  • the computational load of this optimization process is sampling-rate dependent and is of the order of P 3 .
  • Embodiments of the present invention aim to reduce the computational load for waveform concatenation while avoiding concatenation artifacts.
  • speech synthesis systems that are based on small speech segment inventories such as the traditional diphone synthesizers such as L&H TTS-3000TM, and systems based on large speech segment inventories such as the ones used in corpus-based synthesis.
  • digital waveforms, short-time Fourier Transforms, and windowing of speech signals are commonplace in audio technology.
  • Representative embodiments of the present invention provide a robust and computationally efficient technique for time-domain waveform concatenation of speech segments.
  • Computational efficiency is achieved in the synchronization of adjacent waveform segments by calculating a small set of elementary waveform features, and by using them to find the appropriate concatenation points. These waveform-deduced features can be calculated offline and stored in moderately sized tables, which in turn can be used by the realtime waveform concatenator. Before and after concatenation, the digital waveforms may be further processed in accordance with methods that are familiar to persons skilled in the art of speech and audio processing. It is to be understood that the method of the invention is carried out in electronic equipment and the segments are provided in the form of digital waveforms so that the method corresponds to the joining of two or more input waveforms into a smaller number of output waveforms.
  • PSOLA synthesis have a relative small inventory of speech segments such as diphone and triphone speech segments.
  • a combination matrix containing the optimal blending anchors E° pt and E 2 pt for each waveform combination can be calculated in advance for all possible speech segment combinations.
  • Phoneme substitution is a technique well known in the art of speech synthesis. Phoneme substitution is applied when certain phoneme combinations do not occur in the speech segment database. If phoneme substitutions occur, then the waveform segments that are to be concatenated have a different phonetic content and the optimal blending anchors are not stored in the phoneme-dependent combination matrices. In order to avoid this problem, substitution should be performed before calculating the combination matrices.
  • Off-line substitution re-organizes the segment lookup data structures that contain the segment descriptors in such a way that the substitution process becomes transparent for the synthesizer.
  • a typical substitution process will fill the empty slots in the segment lookup data structure by new speech segment descriptors that refer to a waveform segment in the database in such a way that the waveform segment resembles more or less to the phonetic representation of the descriptor. It is not necessary to construct combination matrices for unvoiced phonemes such as unvoiced fricatives. This may further lead to a significant but language-dependent memory saving.
  • the above minimization criterion treats the two waveforms independently (absence of cross term), enabling the process for off-line calculation.
  • the first blending anchor E x is determined by minimizing
  • the second blending anchor E 2 is determined by minimizing . In the following, these will be called the minimum energy anchors.
  • the above terms would be calculated for different values of E x and E 2 in the optimization interval. That is time-consuming.
  • the two optimization intervals over which E l and E 2 may vary are convex intervals.
  • the weighted energy calculation can be calculated as a sliding weighted energy, and this is a candidate for optimization.
  • x is the signal from which to compute the sliding weighted energy.
  • the weighting is done by means of a point-wise multiplication of the signal x by a window.
  • a recursive formulation of the modulated energy term can be obtained by means of some simple math, based on some well-known trigonometric relations:
  • N and 2M are of the same order and much larger than 10. This means that the
  • the time position of the largest peak or trough of the low-pass filtered waveform in the local neighborhood of the join is used in the waveform similarity process.
  • the waveform similarity process may synchronize the left and right signal based on the position of the largest peak instead of using an expensive cross-correlation criterion.
  • the low-pass filter serves to avoid picking up spurious signal peaks that may differ from the peak corresponding to the (lower) harmonics contributing most to the signal power of the voiced speech.
  • the order of the low-pass filter is moderate to low and is sampling-rate dependent.
  • the low-pass filter may be implemented as a multiplication-free nine-tap zero-phase summator for speech recorded at a sampling-rate of 22 kHz.
  • the decision to synchronize on the largest peak or trough depends on the polarity of the recorded waveforms.
  • voiced speech is produced during exhalation resulting in a unidirectional glottal airflow causing a constant polarity of the speech waveforms.
  • the polarity of the voiced speech waveform can be detected by investigating the direction of pulses of the inverse filtered speech signal (i.e. residual signal), and may often also be visible by investigating the speech waveform itself.
  • the polarity of any two speech recordings is the same despite the non stationary character of the speech as long as certain recording conditions remain the same, among others: the speech is always produced on exhalation and the polarity of the electric recording equipment is unchanged in time.
  • the waveforms of the voiced segments to be concatenated should have the same polarity.
  • the recording equipment settings that control the polarity change over time it is still possible to transform the recorded speech waveforms that are affected by a polarity change by multiplying the sample values by minus one, such that their polarity is of all recordings is the same.
  • Listening experiments indicate that the best concatenation results are obtained by synchronization based on the largest peaks, if the largest peaks have higher average magnitude than the lowest troughs (this observed over many different speech signals recorded with the same equipment and recording conditions, for example, a single speaker speech database).
  • the lowest troughs are considered for synchronization.
  • those peaks or troughs used for synchronization are called the synchronization peaks.
  • the troughs are then regarded as negative peaks.
  • Listening experiments further indicate that waveform synchronization based on the location of the synchronization peaks alone results in a substantial improvement compared with unsynchronized concatenation. A further improvement in concatenation quality can be achieved by combining the minimum energy anchors with the synchronization peaks.
  • Figure 4 shows the left speech segment in the neighborhood of the join J.
  • the join J identifies an interval where concatenation can take place. The length of that interval is typically in the order of one to more pitch periods and is often regarded as a constant.
  • the weighted energy, the low-pass filtered signal and the weighted signal (fade-out) are also shown. For reasons of clarity, the signals are scaled differently.
  • Figure 4 helps to understand the process of determining the anchors of the left segment.
  • Time-index D indicates the location of minimum weighted energy in the neighborhood of the join J. This is the so- called minimum energy anchor as defined above. In this particular case, it is assumed that the first blending anchor is taken as that minimum energy anchor (A more detailed discussion on the anchor selection can be found in the algorithm descriptions below).
  • the middle of the concatenation zone is assumed to correspond to the blending anchor D.
  • Time-index A from Figure 4 corresponds with the start of the concatenation zone (i.e. fade-out interval), and time-index B indicates the end of the concatenation zone.
  • D corresponds to A plus the half of the fade-out interval.
  • C is the time-index corresponding to the synchronization peak in the neighborhood of the minimum energy anchor.
  • the fade-in and fade-out intervals have the same length as they are overlapped during waveform blending to form the concatenation zone.
  • the left and right optimization zones for both segments are assumed to be known in advance, or to be given by the application that uses segment concatenation. For example, in a diphone synthesizer the optimization zone of the left (i.e.
  • first waveform corresponds to the region (typically in the nucleus part of the right phoneme of the diphone) where the diphone may be cut
  • optimization zone of the right (i.e. second) waveform corresponds to the location of the left phoneme of the right diphone where the diphone may be cut.
  • An implementation of the synchronization algorithm to concatenate a left and a right waveform segment consists of the following steps:
  • the optimization zone is preferably a convex interval around the join that has a length of at least one pitch period.
  • the "neighborhood" of a minimum energy anchor corresponds to a convex interval that includes the minimum energy anchor and that has preferably a length of at least one pitch period.
  • a first blending anchor is chosen as the minimum energy anchor that corresponds to the lowest energy. This choice minimizes one of the minimum energy conditions.
  • the other blending anchor that resides in the other speech waveform segment is chosen in such a way that the synchronization peaks coincide when the waveforms are (partly) overlapped in the concatenation zone prior to blending.
  • the algorithm may also work if the synchronization does not take into account the value of the minimum weighted energy of the two minimum energy anchors (as described in step 3). This corresponds to blind assignment of a minimum energy anchor to a blending anchor. In this approach one (left or right) minimum energy anchor is systematically chosen as the blending anchor. In this case, the calculation of the other minimum energy anchor is superfluous and can thus be omitted.
  • the length of the concatenation zone is taken as the maximum pitch period of the speech of a given speaker; however, it is not necessary to do so.
  • the function of the synchronization peak and the minimum energy anchors can be switched:
  • the two minimum energy anchors are searched for in the (close) neighborhood of the two synchronization peaks obtained in step 1. .
  • the close "neighborhood" of a synchronization peak corresponds to a convex interval that includes the synchronization peak and that has a length preferably larger than one pitch period.
  • a first blending anchor is chosen as the minimum energy anchor that corresponds to the lowest energy. This choice minimizes one of the minimum energy conditions.
  • the other blending anchor that resides in the other speech waveform segment is chosen in such a way that the synchronization peaks coincide when the waveforms are partly overlapped in the concatenation zone prior to blending.
  • the algorithm can also work if the synchronization does not take into account the value of the minimum weighted energy corresponding to the two minimum energy anchors (as described in step 3). This corresponds to a blind assignment of a minimum energy anchor to a blending anchor. In this approach one (left or right) minimum energy anchor is systematically chosen as the blending anchor.
  • the synchronization peak may be used such as the maximum peak of the derivative of the low-pass filtered speech signal, or the maximum peak of the low-pass filtered residual signal that is obtained after LPC inverse filtering.
  • FIG. 2 A functional diagram of the speech waveform concatenator is given in Figure 2, which shows the synchronization and blending process.
  • a part of the trailing edge of the left (first) waveform segment, larger than the optimization zone, is stored in buffer 200.
  • the part of the leading edge of the second waveform segment of a size, larger than the optimization zone is stored in a second buffer 201.
  • the minimum energy anchor of the waveform in the buffer 200 is calculated in the minimum energy detector 210, and this information is passed on to the waveform blender /synchronizer 240 together with the value of the minimum weighted energy at the minimum energy anchor.
  • the minimum energy detector 211 performs a search to detect the minimum energy anchor point of the waveform stored in buffer 201 and passes it on together with the corresponding weighted energy value to the waveform blender /synchronizer 240. (In another embodiment of the invention, only one of the two minimum energy detectors 210 or 211 are used to select the first blending anchor.) For some applications, such as TTS, the position of the n nimum energy anchors can be stored off-line, resulting in a faster synchronization. In the latter case, the minimum energy detection process is equivalent to a table lookup.
  • the waveform from buffer 200 is low-pass filtered with a zero-phase filter 220 to generate another waveform.
  • This new waveform is then subjected to a peak-picking search 230 taking into account the polarity of the waveforms (as described above).
  • the location of the maximum peak is passed to the waveform blender /synchronizer 240.
  • the same processing steps are carried out by the zero-phase low-pass filter 221 and peak detector 231, which results in the location of the other synchronization peak. This location is send to the waveform blender /synchronizer 240.
  • the waveform blender /synchronizer 240 selects a first blending anchor based on the energy values, or based on some heuristics and a second blending anchor based on the alignment condition of the synchronization peaks.
  • the waveform blender /synchronizer 240 overlaps the fade-out interval of the left (first) waveform segment and the fade-in region of the right (second) waveform segment that are obtained from the buffers 200 and 201, before weighting and adding them.
  • the weighting and adding process is well known in the art of speech processing and is often referred to as (weighted) overlap-and-add processing.
  • the minimum energy anchors are stored because of the large gain in computational efficiency and because they are independent of the adjoining waveform.
  • the computational load may be reduced by storing those features in tables.
  • Most TTS systems use a table of diphone or polyphone boundaries in order to retrieve the appropriate segments. It is possible to "correct" this polyphone boundary table by replacing the boundaries by their closest minimum energy anchor. In the case of a TTS system, this approach requires no additional storage and reduces the CPU load for synchronization significantly.

Abstract

L'invention concerne un procédé de synthèse de la parole par concaténation, qui sert à chaîner efficacement des segments de forme d'onde dans le domaine temporel. Un dispensateur de forme d'onde numérique produit une suite de segments de forme d'onde d'entrée. Un concaténateur chaîne les segments d'entrée en utilisant un mélange de formes d'onde dans une zone de concaténation afin de synchroniser, pondérer, et ajouter par chevauchement des parties choisies des segments d'entrée pour produire une forme d'onde numérique unique. La synchronisation consiste à déterminer un point d'ancrage énergétique minimal pondéré dans la partie choisie de chaque segment d'entrée, et à aligner des crêtes de synchronisation à proximité de chaque point d'ancrage.
PCT/US2001/028672 2000-09-15 2001-09-14 Synchronisation rapide de la forme d'onde pour la concatenation et la modification a echelle de temps de la parole WO2002023523A2 (fr)

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EP01970936A EP1319227B1 (fr) 2000-09-15 2001-09-14 Synchronisation rapide de la forme d'onde pour la concatenation et la modification a echelle de temps de la parole
AU2001290882A AU2001290882A1 (en) 2000-09-15 2001-09-14 Fast waveform synchronization for concatenation and time-scale modification of speech
DE60127274T DE60127274T2 (de) 2000-09-15 2001-09-14 Schnelle wellenformsynchronisation für die verkettung und zeitskalenmodifikation von sprachsignalen

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US60/233,031 2000-09-15

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